OPTIMAL STRATEGY FOR CYCLIC STEAM STIMULATION OIL PRODUCTION: A MATHEMATICAL MODEL
Bibliographic record
Abstract
A central issue of managing a heavy oil pro- duction usingcyclic steam stimulation process is whether an optimal strategy for adding new wells and removing old wells can be found. In this paper we present a mathematical model which provides a general framework for this type of problem. With suitable assumptions, we formulate the production pro- cess as a constrained optimal control problem in continuous time or equivalently a constrained linear programming prob- lem in discrete time. The main advantage of the model is that the size of the problem is not necessarily related to the scale of the operations (number of wells). A numerical algorithm based on the model shown to be efficient is given, as well as results for two test cases of large scale projects. 1. Introduction. Cyclic steam stimulation process is commonly used to produce heavy oil from oil-sand formations. High pressure steam is generated at central plant facilities. The steam is distributed through a pipeline system and injected into reservoirs away from the central plant facilities. Steam injection continues until the oil viscosity is reduced to a level such that the oil can be pumped to the surface. The oil, water and gas mixture is produced during the production part of the cycle and is returned to the central plant facilities. Water is separated from the mixture, treated, mixed with make-up water and reused as feed water for the steam generators. The produced gas supplements the purchased natural gas as fuel for the steam generators. The produced oil is processed by removing sand and diluted with a lighter hydrocarbon (diluent) to meet pipeline viscosity specifications. The diluted bitumen is sold to refineries. For details of the process, readers are referred to (2). Typically central plant facilities are built in several locations, sur- rounded by wells drilled at various stages of the operation. Each well is associated with a single plant. The older wells may have completed up to 10 cycles of steaming and production. New wells are periodically added. The duration of the steaming and production phases depends
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".